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Expert Review of Proteomics Volume 12, 2015 - Issue 6
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Perspectives

The potential clinical impact of the release of two drafts of the human proteome

Iakes EzkurdiaUnidad de Proteómica, Centro Nacional de Investigaciones Cardiovasculares, CNIC, Madrid, Spain
,
Enrique CalvoUnidad de Proteómica, Centro Nacional de Investigaciones Cardiovasculares, CNIC, Madrid, Spain
,
Angela Del PozoInstituto de Genetica Medica y Molecular, Hospital Universitario La Paz, Madrid, Spain
,
Jesús VázquezLaboratorio de Proteómica Cardiovascular, Centro Nacional de Investigaciones Cardiovasculares, CNIC, Madrid, Spain
,
Alfonso ValenciaStructural Biology and Bioinformatics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain;National Bioinformatics Institute (INB), Spanish National Cancer Research Centre (CNIO), Madrid, Spain
&
Michael L. TressStructural Biology and Bioinformatics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, SpainCorrespondence[email protected]
Pages 579-593 | Published online: 23 Oct 2015

References

•• One of the two papers studied in depth for this article. A proteomics analysis carried out wholly on tissues and hematopoietic cells.

•• The other paper that is the subject of this article. The tissue and fluid proteomics experiments were only a small part of this study.

  • Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science. 2001;291:1304−1351.
  • International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001;409:860−921.
  • Koenig T, Menze BH, Kirchner M, et al. Robust prediction of the MASCOT score for an improved quality assessment in mass spectrometric proteomics. J Proteome Res. 2008;7:3708–3717.
  • Cox J, Neuhauser N, Michalski A, et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10:1794–1805.
  • UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:D204–212.
  • Eng JK, McCormack AL, Yates JR. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. Am Soc Mass Spectrom. 1994;5:976–989.
  • Pruitt KD, Brown GR, Hiatt SM, et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014;42:D756–763.
  • Farrah T, Deutsch EW, Hoopmann MR, et al. The state of the human proteome in 2012 as viewed through PeptideAtlas. J Proteome Res. 2013;12:162–171.
  • Fenyö D, Eriksson J, Beavis R. Mass spectrometric protein identification using the global proteome machine. Methods Mol Biol. 2010;673:189–202.
  • Shiromizu T, Adachi J, Watanabe S, et al. Identification of missing proteins in the neXtProt database and unregistered phosphopeptides in the PhosphoSitePlus database as part of the chromosome-centric human proteome project. J Proteome Res. 2013;12:2414–2421.
  • Geiger T, Wehner A, Schaab C, et al. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol Cell Proteomics. 2012;11:M111.014050–M111.014050.
  • Ezkurdia I, Juan D, Rodriguez JM, et al. Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum Mol Genet. 2014;23:5866–5878.

•• A paper that is a counterpoint to the twoNature articles. The authors found that proteomics analyses detect peptides from the most ancient genes and very few from recently evolved genes. Proteins the two Nature studies claimed to have detected will have been removed from the reference genome as a result of this article

  • Harrow J, Frankish A, Gonzalez JM, et al. GENCODE: the reference human genome annotation for the ENCODE project. Genome Res. 2012;22:760–774.
  • NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2015;43:D16–17.
  • Verbeurgt C, Wilkin F, Tarabichi M, et al. Profiling of olfactory receptor gene expression in whole human olfactory mucosa. PLoS One. 2014;9:e96333.
  • Deutsch EW, Sun Z, Campbell D, et al. The state of the human proteome in 2014/2015 as viewed through PeptideAtlas: enhancing accuracy and coverage through the AtlasProphet. J Proteome Res. 2015;14:3461–3473.

•• Another contrast to the two Nature papers. The PeptideAtlas update very elegantly finds that the two studies add no more than 500 proteins to those already identified in experiments on cell lines.

  • Ezkurdia I, Vázquez J, Valencia A, et al. Analyzing the first drafts of the human proteome. J Proteome Res. 2014;13:3854–3855.
  • Ezkurdia I, Vázquez J, Valencia A, et al. Correction to “Analyzing the first drafts of the human proteome”. J Proteome Res. 2015;14:1991.
  • Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods. 2007;4:207–214.

• One of the first papers to propose the calculation of false positive rates using decoy peptides.

  • Nesvizhskii AI. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J Proteom. 2010;73:2092–1223.

• A detailed review of the use of false discovery rates in proteomics experiments, showing how errors are amplified when going from peptide to protein level.

  • Serang O, Käll L. Solution to statistical challenges in proteomics is more statistics, not less. J Proteome Res. 2015;14:4099–4103.
  • Reiter L, Claassen M, Schrimpf SP, et al. Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol Cell Proteomics. 2009;8:2405–2417.
  • Savitski MM, WIlhelm M, Hahne H, et al. A scalable approach for protein false discovery rate estimation in large proteomic data sets. Mol Cell Proteomics. 2015. DOI:10.1074/mcp.M114.046995.
  • Gaudet P, Michel PA, Zahn-Zabal M, et al. The neXtProt knowledgebase on human proteins: current status. Nucleic Acids Res. 2015;43:D764–770.
  • Colaert N, Van Huele C, Degroeve S, et al. Combining quantitative proteomics data processing workflows for greater sensitivity. Nat Methods. 2011;8:481–483.
  • Paulo JA. Practical and efficient searching in proteomics: a cross engine comparison. Webmedcentral. 2013;4:WMCPLS0052.
  • Carr S, Aebersold R, Baldwin M, et al. The need for guidelines in publication of peptide and protein identification data: working group on publication guidelines for peptide and protein identification data. Mol Cell Proteomics. 2004;3:531–533.
  • Omenn GS, States DJ, Adamski M, et al. Overview of the HUPO plasma proteome project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics. 2005;5:3226–3245.
  • Shteynberg D, Nesvizhskii AI, Moritz RL, et al. Combining results of multiple search engines in proteomics. Mol Cell Proteomics. 2013;12:2383–2393.
  • White FM. The potential cost of high-throughput proteomics. Sci Signal. 2011;4:pe8.

• This paper sets out the potential harmful effects of combining large-scale high-throughput proteomics and insufficiently validated data.

• Explains how recent advances in high-throughput proteomics can easily lead to identifying peptides that do not exist.

  • Bonzon-Kulichenko E, Garcia-Marques F, Trevisan-Herraz M, et al. Revisiting peptide identification by high-accuracy mass spectrometry: problems associated with the use of narrow mass precursor windows. J Proteome Res. 2015;14:700–710.

• The authors set out solutions for the problems identified in Ref. [33]

  • Omenn GS, Lane L, Lundberg EK, et al. Metrics for the human proteome project 2015: progress on the human proteome and guidelines for high-confidence protein identification. J Proteome Res. 2015;14:3452–3460.

•• Details many of the shortcomings of the two Nature analyses and addresses the state of the art in protein detection.

  • Horvatovich P, Lundberg EK, Chen YJ, et al. Quest for missing proteins: update 2015 on chromosome-centric human proteome project. J Proteome Res. 2015;14:3415–3431.
  • Nesvizhskii AI. Proteogenomics: concepts, applications and computational strategies. Nat Methods. 2014;11:1114–1125.

• The paper discusses the concepts and potential pitfalls of proteogenomics studies in considerable detail.

  • Krug K, Carpy A, Behrends G, et al. Deep coverage of the Escherichia coli proteome enables the assessment of false discovery rates in simple proteogenomic experiments. Mol Cell Proteomics. 2013;12:3420–3430.
  • Ross PL, Huang YN, Marchese JN, et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics. 2004;3:1154–1169.
  • Ezkurdia I, Rodriguez JM, Carrillo-de Santa Pau E, et al. Most highly expressed protein-coding genes have a single dominant isoform. J Proteome Res. 2015;14:1880–1887.
  • Abascal F, Ezkurdia I, Rodriguez-Rivas J, et al. Alternatively spliced homologous exons have ancient origins and are highly expressed at the protein level. PLoS Comput Biol. 2015;11:e1004325.
  • Huang Da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57.
  • Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347:1260419.
  • Zhang B, Wang J, Wang X, et al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014;513:382–387.

• This large-scale study concentrates on cancer cells instead of tissues. Combining large-scale proteomics analysis of healthy and diseased cells has promise for the detection of biomarkers.

  • Narayanan R. Phenome-genome association studies of pancreatic cancer: new targets for therapy and diagnosis. Cancer Genom Proteom. 2015;12:9–19.
  • Narayanan R. Ebola-associated genes in the human genome: implications for novel targets. MOJ Proteom Bioinform. 2015;1:00032.
  • Shao S, Guo T, Aebersold R. Mass spectrometry-based proteomic quest for diabetes biomarkers. Biochim Biophys Acta. 2015;1854:519–527.

• In this work, the authors review the current status of diabetes mellitus biomarker discovery through different mass spectrometry techniques.

• A review detailing recent advances in the discovery of protein biomarkers via proteomics and the difficulties of validating these biomarkers.

  • Aebersold R, Bader GD, Edwards AM, et al. The biology/disease-driven human proteome project (B/D-HPP): enabling protein research for the life sciences community. J Proteome Res. 2013;12:23–27.
  • Zhang K, Fu Y, Zeng WF, et al. A note on the false discovery rate of novel peptides in proteogenomics. Bioinformatics. 2015;31:3249–3253.
  • Ma J, Ward CC, Jungreis I, et al. Discovery of human sORF-encoded polypeptides (SEPs) in cell lines and tissue. J Proteome Res. 2014;13:1757–1765.
  • Vanderperre B, Lucier JF, Bissonnette C, et al. Direct detection of alternative open reading frames translation products in human significantly expands the proteome. PLoS One. 2013;8:e70698.
  • Brusniak MY, Chu CS, Kusebauch U, et al. An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology. Proteomics. 2012;12:1176–1184.
  • Hao Y, Colak R, Teyra J, et al. Semi-supervised learning predicts approximately one third of the alternative splicing isoforms as functional proteins. Cell Rep. 2015;12:183–189.
  • Fu Y, Qian X. Transferred subgroup false discovery rate for rare post-translational modifications detected by mass spectrometry. Mol Cell Proteomics. 2014;13:1359–1368.
  • Chu Q, Ma J, Saghatelian A. Identification and characterization of sORF-encoded polypeptides. Crit Rev Biochem Mol Biol. 2015;50:134–141.
  • Guttman M, Russell P, Ingolia NT, et al. Ribosome profiling provides evidence that large noncoding RNAs do not encode proteins. Cell. 2013;154:240–251.

• The authors find that lincRNA behave differently from protein coding transcripts when passing through the ribosome

  • Ruiz-Orera J, Messeguer X, Subirana JA, et al. Long non-coding RNAs as a source of new peptides. Elife. 2014;3:e03523.
  • Griss J, Perez-Riverol Y, Hermjakob H, et al. Identifying novel biomarkers through data mining-a realistic scenario? Proteom Clin Appl. 2015;9:437–443.
  • Khatun J, Yu Y, Wrobel JA, et al. Whole human genome proteogenomic mapping for ENCODE cell line data: identifying protein-coding regions. BMC Genom. 2013;14:141.

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